Citations

AbstractObjectiveTo report on a series of patients with cANCA/PR3‐positive, granulomatosis with polyangiitis (GPA)‐associated subglottic stenosis (SGS) and evaluate response to medical maintenance therapy with rituximab versus other immunosuppressants following initial endoscopic laser excision.Study DesignRetrospective chart review.SettingTertiary academic center.MethodsA retrospective chart review of patients with SGS and cANCA/PR3‐positive GPA who received immunosuppressive maintenance therapy following endoscopic laser excision at our institution from June 1989 to March 2020 was performed. Data pertaining to patient demographics, clinical features, medications, and endoscopic laser procedures were collected.ResultsA total of 27 patients (15 women) with mean age (range) of 40 (19‐59) years and mean (range) follow‐up of 12.6 years (1.5‐28.6) were identified. Sixteen patients (60%) had limited GPA. Six patients (24%) had previously received local intervention with open surgery (n = 1, 4%) or endoscopic techniques (n = 5, 20%). All patients experienced symptom improvement following initial CO2 laser excision at our institution without any procedural complications or adverse events. Following initial laser excision, 15 patients (60%) were treated with rituximab and 10 patients (40%) were treated with nonrituximab immunosuppressive agents. Patients treated with rituximab were less likely to recur (P = 0.040). Limited GPA was associated with an increased incidence of recurrence (P = 0.031). Median time (years) to recurrence (range) was 3.2 (0.3‐19.3) and was not significantly associated with treatment or GPA subtype.ConclusionEndoscopic CO2 laser excision is a safe and effective local intervention for GPA‐associated SGS. Medical maintenance therapy with rituximab reduces risk of recurrence following initial laser excision relative to treatment with non‐rituximab agents.

Abstract Purpose: There is a need to improve current risk stratification of stage II colorectal cancer to better inform risk of recurrence and guide adjuvant chemotherapy. We sought to examine whether integration of QuantCRC, a digital pathology biomarker utilizing hematoxylin and eosin–stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines. Experimental Design: ASCO and QuantCRC-integrated schemes were applied to a cohort of 398 mismatch-repair proficient (MMRP) stage II colorectal cancers from three large academic medical centers. The ASCO stage II scheme was taken from recent guidelines. The QuantCRC-integrated scheme utilized pT3 versus pT4 and a QuantCRC-derived risk classification. Evaluation of recurrence-free survival (RFS) according to these risk schemes was compared using the log-rank test and HR. Results: Integration of QuantCRC provides improved risk stratification compared with the ASCO scheme for stage II MMRP colorectal cancers. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared with the ASCO scheme (62.5% vs. 42.2%) without compromising excellent 3-year RFS. The QuantCRC-integrated scheme provided larger HR for both intermediate-risk (2.27; 95% CI, 1.32–3.91; P = 0.003) and high-risk (3.27; 95% CI, 1.42–7.55; P = 0.006) groups compared with ASCO intermediate-risk (1.58; 95% CI, 0.87–2.87; P = 0.1) and high-risk (2.24; 95% CI, 1.09–4.62; P = 0.03) groups. The QuantCRC-integrated risk groups remained prognostic in the subgroup of patients that did not receive any adjuvant chemotherapy. Conclusions: Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance for stage II MMRP colorectal cancers.

Background The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug‐eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. Methods and Results We developed and validated a new AI‐based pipeline using retrospective data of drug‐eluting stent‐treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de‐identified Clinformatics Data Mart Database (n=9978). The 36 months following drug‐eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI‐DAPT model. The AI‐DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%–92%] for ischemia and 84% [95% CI, 82%–87%] for bleeding predictions. Conclusions Our AI‐DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

686 Background: Immune checkpoint inhibitors (ICI) have been used to treat advanced muscle invasive and/or metastatic bladder cancer. However, ICIs are only effective in 30-40% of cases. In light of potentially significant IRAE’s, tools to predict individual patient level benefits are desirable. AI technology offers the opportunity to apply computational methods to predict outcomes from readily available digitized pathology slides. We investigated the utility of an AI platform integrating computational biomarkers based on morphological characterization of digitized H&E slide whole images (WSI) to predict response to ICI. Methods: We analyzed H&E-stained whole slide images (WSI) of bladder tumor tissue collected from transurethral resection of bladder tumor (TURBT) from 116 patients with advanced or metastatic bladder cancer at a single institution between 2015-2020. Adjacent multiplex IHC stained specimens were analyzed myeloid and lymphoid marker panels. 20 patients from the overall cohort treated with ICI were designated ‘responders’ (complete response, partial response, stable disease) and 20 patients were identified as non-responders (progressive disease) based on ICI best clinical/radiographic response. WSIs were divided into small non-overlapping image patches. These image tiles were processed into multiple AI encoder models to extract morphological features. The tile morphological features, represented by high dimensional vectors, were combined by an aggregation model to represent the whole slide morphology and then used to classify the patients into responders vs non-responders to immunotherapy. Area-under-the-receiver operating characteristic (AUC) was used to measure the performance of response prediction. Results: Our method shows AUC of 0.708 at classification of the patients (n=40) into responder and non-responder groups. With a cutoff that identifies the top 50% patients (n=20) of high probability of responding to immunotherapy predicted by our models, 75% of them are responders. In the bottom 50% patients (n=20) of the low scores, 75% of them are non-responders. . Multiplex IHC (myeloid and lymphoid panel) was performed using adjacent sections and were characterized yielding deeper mechanistic insight into the inflammatory populations driving predictive morphologic patterns. Conclusions: By applying innovative AI morphological analysis on patient WSIs, we generated a computational biomarker that shows promising ability to predict patient response to immunotherapy in bladder cancer. This approach merits future prospective validation and additional patient level analysis in broader cohorts. [Table: see text]

162 Background: There is a need to improve current risk stratification of stage II and III colorectal cancer (CRC) to better inform risk of recurrence and guide adjuvant chemotherapy. The purpose of this study is to examine whether integration of QuantCRC, an AI-based digital pathology biomarker utilizing hematoxylin and eosin-stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines. Methods: ASCO and QuantCRC-integrated risk schemes were applied to an observational cohort of 1,068 stage II and III CRCs. The stage II integrated scheme utilizes pT3 vs. pT4 and QuantCRC-derived risk groups. The stage III integrated scheme utilizes pT1-3 vs. pT4, pN1 vs. pN2, and QuantCRC-derived risk groups. Performance metrics included log-rank test, hazard ratios and Somers’ Dxy rank correlation. Results: Integration of QuantCRC provides improved risk stratification compared to the ASCO scheme for stage II and III CRC. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared to the ASCO scheme (69.3% vs. 60.4%) without decreased 3-year RFS. The QuantCRC-integrated scheme provided larger hazard ratios (HR) for both intermediate-risk (3.04, 95%CI 1.81-5.10, P=2.8x10-5) and high-risk (4.62, 95%CI 2.02-10.61, P=0.0003) groups compared to ASCO intermediate-risk (2.09, 95%CI 1.21-3.63, P=0.008) and high-risk (3.08, 95%CI 1.57-6.01, P=0.001) groups. The QuantCRC-integrated scheme for stage III tumors identified a small group of 80/518 (15.4%) CRCs at very high risk of recurrence with HR of 4.08 (95%CI 2.68-6.23, P=6.5x10-11) compared to a HR of 2.42 (95%CI 1.72-3.38, P=3.1x10-7) for 228/518 (44.0%) high-risk CRCs in the ASCO scheme. QuantCRC-integrated risk groups remained prognostic in stage III CRCs when stratified by presence or absence of any adjuvant chemotherapy. No difference in RFS were seen in QuantCRC-integrated low-risk and intermediate-risk stage III CRCs stratified by 3 vs. 6-months of oxaliplatin-based adjuvant chemotherapy suggesting that these two groups can be treated with 3-months of adjuvant therapy. Conclusions: Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance.

170 Background: KRAS-mutant colorectal cancer (CRC) has a worse prognosis and greater resistance to therapy, attracting tremendous efforts to develop both allele-specific and pan-KRAS targeting agents including KRAS G12C and G12D inhibitors. CRC has demonstrated both primary and acquired resistance to KRAS G12C inhibitors, which can be better evaluated by circulating tumor DNA (ctDNA)-based next generation sequencing (NGS) methods without tissue sampling bias. Here, we utilized clinical ctDNA data to delineate the landscape of candidate alterations associated with primary resistance to KRAS G12D targeting in CRC. Methods: Samples from advanced CRC patients with Guardant360 data in 2021 were categorized into a KRAS G12D group and comparator groups of KRAS G12C and KRAS not detected (ND) according to KRAS mutation status. Demographics, MSI-H status, and blood tumor mutational burden (bTMB) were summarized and compared between groups using t-test for continuous variables and Fisher’s exact test for categorical variables. Co-occurrence patterns of alterations between KRAS and other genes were assessed. Primary resistance mutations were inferred by acquired resistance findings to KRAS G12C inhibitors reported by Awad et al. N Engl J Med. 2021 and Tanaka et al. Cancer Discov. 2021. Results: Among approximately 2500 samples, 16.8% had KRAS G12D, the most common KRAS mutation in CRC. The mean allele frequency of KRAS G12D (11.4%) is comparable to that of KRAS G12C (10.9%). Mean age (61.6 years), MSI-H frequency (3.8%), and mean bTMB (14.2 Mut/Mb) of KRAS G12D group is comparable to those of KRAS ND group. This contrasts with KRAS G12C with significantly lower mean age (60.2 years, p=0.01), lower MSI-H frequency (0.56%, p=0.03), and higher mean bTMB (16.3 Mut/Mb, p=0.02) compared to KRAS ND. Both KRAS G12D and G12C significantly co-occur with APC, PIK3CA, and SMAD4 alterations. FBXW7, AR, and KEAP1 alterations were unique co-occurrences to KRAS G12D, while KIT was unique to KRAS G12C. Candidate alterations associated with primary resistance in KRAS G12D CRC included simultaneously present mutations of KRAS Q61H (14.1%), G12V (6.4%), G12C (5.9%), G13D (3.6%), BRAF V600E (2.7%) and others, amplification of MET (2%), MYC (1.6%), and KRAS (1.4%), and FGFR3-TACC3 translocation (Table). Conclusions: KRAS G12D is associated with unique co-occurring molecular alterations compared to KRAS G12C in CRC. ctDNA-based NGS platforms can survey candidate alterations associated with primary resistance to KRAS G12D targeting in CRC. Further validation in preclinical models is needed. [Table: see text]

Abstract Background: Venous thromboembolism (VTE) in pregnancy is a major cause of maternal morbidity and mortality, and the use of preventive low-molecular-weight heparin (LMWH) can be challenging. Clinical guidelines recommend eliciting pregnant individuals’ preferences towards the use of daily injections of LMWH and discussing the best option through a shared decision-making (SDM) approach. Our aim was to identify individuals’ preferences concerning each of the main clinical outcomes, and categorize attributes influencing the use of LMWH during pregnancy. Methods: Design: Convergent mixed-methods. Participants: Pregnant women or those planning a pregnancy with VTE recurrence risk. Intervention: A SDM intervention about thromboprophylaxis with LMWH in pregnancy. Analysis: Quantitatively, we report preference scores assigned to each of the health states. Qualitatively, we categorized preference attributes using Burke’s pentad of motives framework: scene, agent, agency, act, and purpose. We use mixed-method convergent analysis to report findings using side-by-side comparison of concordance/discordance. Results: The least valued health state was to experience a pulmonary embolism (PE), followed by major obstetrical bleeding (MOB), deep vein thrombosis (DVT), and using daily injections of LMWH (valued as closest to a ‘healthy pregnancy’). Women's previous experiences, access to care (scene) and shared decision-making (agent) affected preferences. LMWH's benefits were noted, but substantial drawbacks described (agency). The main goal was avoiding the risk of VTE (purpose). Side-by-side comparisons revealed concordance between motives and DVT and PE health states. Discordance appeared between using daily injections of LMWH and agent- and agency motives and between MOB and the agency motive. Conclusions: Mixed-methods provide a nuanced understanding of women's LMWH preferences, by quantifying health states preferences and exploring attributes qualitatively. Incorporating both methods may improve patient-centered care around preference-sensitive decisions in thromboprophylaxis during pregnancy.

Background: Radiographic surveillance of colorectal cancer (CRC) after curative-intent therapy is costly and unreliable. Methylated DNA markers (MDMs) detected primary CRC and metastatic recurrence with high sensitivity and specificity in cross-sectional studies. This study evaluated using serial MDMs to detect recurrence and monitor the treatment response to anti-cancer therapies. Methods: A nested case-control study was drawn from a prospective cohort of patients with CRC who completed curative-intent therapy for CRC of all stages. Plasma MDMs were assayed vis target enrichment long-probe quantitative-amplified signal assays, normalized to B3GALT6, and analyzed in combination with serum carcinoembryonic antigen to yield an MDM score. Clinical information, including treatment and radiographic measurements of the tumor burden, were longitudinally collected. Results: Of the 35 patients, 18 had recurrence and 17 had no evidence of disease during the study period. The MDM score was positive in 16 out of 18 patients who recurred and only 2 of the 17 patients without recurrence. The MDM score detected recurrence in 12 patients preceding clinical or radiographic detection of recurrent CRC by a median of 106 days (range 90–232 days). Conclusions: Plasma MDMs can detect recurrent CRC prior to radiographic detection; this tumor-agnostic liquid biopsy approach may assist cancer surveillance and monitoring.

Introduction: MDS are primarily diseases of the elderly, with median age at diagnosis of 72 years. Focused data on clinical and molecular characteristics, as well as outcomes, in younger pts under age 60 years are limited. We conducted this multicenter retrospective study to describe pt characteristics, cytogenetic and molecular profiles, treatment modalities, and outcomes in younger pts with MDS, including adolescent and young adults (AYA). Methods: The study included pts diagnosed with MDS between 2002-2023 at 7 academic centers. We compared baseline characteristics, treatment, and outcomes of AYA (18-39 years) and younger adult (YA) pts (40-59 years). Responses were assessed using the modified IWG 2006 response criteria which included complete remission (CR), marrow CR (mCR), and hematologic improvement (HI). Wilcoxon rank sum test and Fisher's exact (and Chi-square) test were used to compare pt and disease characteristics for continuous and categorical variables, respectively. Overall survival (OS) was estimated using the Kaplan-Meier method and compared using log-rank test. Results: A total of 173 pts were identified (AYA, N=50; YA, N=123). Median age at diagnosis was 50 years (range, 18-59), with 51% females. Baseline characteristics are shown in Table 1. Therapy-related MDS (t-MDS) was observed in 40 pts (23%) with the remaining classified as de novo MDS (N=133; 77%). Inherited bone marrow failure syndromes were documented in 15 pts (9%), with Shwachman-Diamond Syndrome (N=4) and Fanconi anemia (N=4) being the most frequent. Using the WHO 2016 classification, the most common subtypes of MDS in both the AYA and YA cohorts were MDS with multilineage dysplasia (38% vs 19%), MDS with excess blasts-2 (MDS-EB) (24% vs 29%), and MDS-EB-1 (16% vs 20%). Similarly, using the WHO 2022 classification, the most common subtypes were MDS with low blasts (50% vs 21%), MDS with increased blasts-2 (MDS-IB-2) (24% vs 24%), and MDS-IB-1 (16% vs 18%). Stratifying pts using the IPSS-R cytogenetic (CG) risk group, the most common CG risk category identified was good CG (34% vs 52%, p=0.012). Poor/very poor CG were present in 34% and 37% of the AYA and YA groups, respectively. STAG2 and NRAS mutations occurred more frequently in the AYA pts compared to the YA group, while the YA cohort was enriched for the following mutations: TP53, DNMT3A, SF3B1, SRSF2, and TET2 (Figure 1). In the AYA population, 18 pts (38%) had very low/low, 10 (21%) intermediate, and 20 pts (42%) had high/very high risk MDS using IPSS-R. In the YA population, 32 pts (26%) had very low/low, 32 (26%) intermediate, and 57 pts (47%) had high/very high risk MDS using IPSS-R. Twenty-seven percent of pts with very low to intermediate-risk MDS using IPSS-R were upstaged to moderate high to very high-risk MDS using IPSS-M. The majority of pts (AYA: 75%; YA: 63%, p=0.5) were transfusion independent at baseline. Single-agent hypomethylating agents were the most frequently used frontline therapy, used in 17 AYA pts (34%) and 58 YA pts (48%), with more AYA pts receiving upfront allogeneic stem cell transplantation (Allo-SCT) compared to YA pts (28% vs 5%). After frontline therapy (Table 1), an additional 14 AYA (28%) and 52 YA (42%) pts received Allo-SCT. In pts with IPSS-R intermediate to very high-risk, response rates (HI+CR+mCR+mCR with HI) to frontline therapies were 46% and 54.5% in the AYA and YA groups, respectively. After median follow-up of 24 months [range, 1-218], median OS was numerically longer in the AYA versus YA group (155 vs 53 months, p=0.2). AML transformation occurred in 24% and 20% of the pts in the AYA and YA groups, respectively. Median OS was significantly longer in de novo compared to t-MDS pts (56 vs 27 months, p=0.03). Median OS among Allo-SCT compared to non-SCT pts were not reached vs 155 months ( p=0.9) in the AYA group and 53 vs 54 months ( p=0.4) in the YA group. IPSS-R (very low+low vs intermediate vs high+very high) was able to distinguish differences in OS in the YA (133 vs 53 vs 17 months, p=0.0002) and AYA (155 vs not reached vs 18 months, p=0.006) pts, with the exception of the intermediate-risk group in the AYA cohort. Conclusions: In this study, d e novoMDS was seen in the majority of younger pts with MDS (both AYA and YA). Mutations in STAG2 and NRAS were more common in the AYA pts, while YA pts had MDS more enriched for TP53, DNMT3A, TET2 and splicing mutations. This study is still ongoing with a plan to compare the abovementioned groups to older pts (≥60 years) with MDS.

Background: We previously reported that treatment with the anti-CD30 antibody-drug conjugate brentuximab vedotin (BV) in combination with the checkpoint inhibitors targeting CTLA-4 and/or PD-1 (ipilimumab (I) and nivolumab (N)) led to high overall response rates (ORR) and complete response (CR) rates in relapsed/refractory (R/R) Hodgkin's lymphoma patients in a Phase 1 ECOG-ACRIN sponsored clinical trial. As previously presented in the Phase 1 component of NCT01896999 (E4412), the ORR for the treatment arms were: BV+I 76%, BV+N 89%, and BV+I+N 82%, with CR rates of 57%, 61%, and 73% across the groups, respectively. This suggested that not all patients who responded derived equal benefit. Moreover, with a median follow-up of 2.24 years, the PFS of 1.1 year, NR, and 2.49 years across the groups suggest that even some patients who achieved CR progressed on or subsequent to treatment. Currently biomarkers have no ability to predict which patients will maximally benefit from these therapies. We investigated blood-based immune biomarkers mechanistically across all cohorts which may explain differential responses and guide future treatment decisions for R/R HL patients. Methods: Peripheral blood plasma for 54 of 61 (89%) patients evaluable for response was collected at up to 4 time points and tested for 92 immuno-oncology soluble analytes with Olink proximity extension assay and for antibody titers to 20 known tumor antigens by ELISA. Matching peripheral blood mononuclear cells (PBMC) were analyzed by CyTOF mass cytometry for major immune cell subsets and marker surface expression, and for T cell receptor diversity by Immunoseq®. Mixed effect linear models were used to identify significantly associated changes (P<0.05) related to treatment longitudinally within groups and to ORR between groups. Results: Analysis of plasma demonstrated durable increase in soluble plasma PD-1 and decreases in CCL17, ANGPT2, MMP12, IL13, and CXCL13 in N-containing regimens, and higher GZMA and CAIX in BV+I (P<0.05). Despite baseline heterogeneity in PBMC composition, we found durable increases in plasmacytoid dendritic cells (pDC) after N-containing treatments, and transient increases in plasmablasts and memory B cells, as well as early decreases in naïve CD4+ T cells and neutrophils across treatments. Treatment with BV+I+N demonstrated late increases in neutrophils, and changes in memory B cell markers. Clinical benefit analyses revealed that elevated soluble CD5, ADA, CXCL13, and CCL17 at baseline or after two treatment cycles were associated with worse ORR (P<0.05), and that a module of co-regulated soluble analytes at baseline, including MUC16, was associated with worse progression free survival (PFS) (P<0.05). NY-ESO-1 autoantibodies were more frequent in non-responders, and the top 10% of expanded TCR clonotypes were increased in responders after one treatment cycle (P<0.05). Conclusion: This data reveals differential immune activation based on treatment modality for patients with R/R HL receiving checkpoint-based regimens in combination with an ADC. Our data highlights potential tumor- and immune-derived predictive and pharmacodynamic biomarker candidates of response and resistance. These novel findings have relevance for R/R HL patients receiving immunotherapy and other immunotherapy combinations such as immuno-chemotherapy platforms. Prospective evaluation of these biomarkers in the Phase II component of this study, a randomized comparison of BV+N vs. BV+N+I which has completed adult accrual, is planned. Figure Legend Clinical responders and non-responder patient samples were analyzed using ELISA (auto antibodies), CyTOF (Cell populations), TCRseq (T cell clonality) and Olink (proteomics). The results showed that pDCs, Neutrophils and B-Plasmablasts increased with specific drug combinations independently of clinical response. While T Cell expansion was associated with clinical response, the increase of auto antibodies against NY-ESO-1 and circulating proteins CD5, ADA, MUC16, CXCL13, CXCL9 and CCL17 were associated with worse prognosis